ExtendAttack: Attacking Servers of LRMs via Extending Reasoning

Large Reasoning Models LRMs have demonstrated promising performance in complex tasks. However, the resource-consuming reasoning processes may be exploited by attackers to maliciously occupy the resources of the servers, leading to a crash, like the DDoS attack in cyber. To this end, we propose a...

A Halpha Metric for Identifying Dormant Black Holes in X-Ray Transients

Dormant black holes BHs in X-ray transients can be identified by the presence of broad Ha emission lines from quiescent accretion discs. Unfortunately, short-period cataclysmic variables CVs can also produce broad Ha lines, especially when viewed at high inclinations, and are thus a major source ...

LexiMark: Robust Watermarking via Lexical Substitutions to Enhance Membership Verification of an LLM's Textual Training Data

Large language models LLMs can be trained or fine-tuned on data obtained without the owner's consent. Verifying whether a specific LLM was trained on particular data instances or an entire dataset is extremely challenging. Dataset watermarking addresses this by embedding identifiable modification...

Autonomous 3D Moving Target Encirclement and Interception with Range Measurement

Commercial UAVs are an emerging security threat as they are capable of carrying hazardous payloads or disrupting air traffic. To counter UAVs, we introduce an autonomous 3D target encirclement and interception strategy. Unlike traditional ground-guided systems, this strategy employs autonomous...

Tady: a Neural Disassembler without Structural Constraint Violations

Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this...

Doppelgänger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack

Since the advent of large language models, prompt engineering now enables the rapid, low-effort creation of diverse autonomous agents that are already in widespread use. Yet this convenience raises urgent concerns about the safety, robustness, and behavioral consistency of the underlying prompts,...

Fair Data Exchange with Constant-Time Proofs

The Fair Data Exchange FDE protocol introduced at CCS 2024 offers atomic pay-per-file transfers with constant-size proofs, but its prover and verifier runtimes still scale linearly with the file length n. We collapse these costs to essentially constant by viewing the file as a rate-1 Reed-Solomon...

Evaluation Pipeline for Systematically Searching for Anomaly Detection Systems

Digitalization in the medical world provides major benefits while making it a target for attackers and thus hard to secure. To deal with network intruders we propose an anomaly detection system on hardware to detect malicious clients in real-time. We meet real-time and power restrictions using...

Understanding Content Moderation Policies and User Experiences in Generative AI Products

While recent research has focused on developing safeguards for generative AI GAI model-level content safety, little is known about how content moderation to prevent malicious content performs for end-users in real-world GAI products. To bridge this gap, we investigated content moderation policies...

Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems

The growing deployment of deep learning models in real-world environments has intensified the need for efficient inference under strict latency and resource constraints. To meet these demands, dynamic deep learning systems DDLSs have emerged, offering input-adaptive computation to optimize runtim...

Bridging Unsupervised and Semi-Supervised Anomaly Detection: a Theoretically-Grounded and Practical Framework with Synthetic Anomalies

Anomaly detection AD is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to train classifiers to distinguish normal data from synthetic anomalies. We extend this principle to semi-supervised AD,...

Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning

Federated learning FL shows great promise in large-scale machine learning but introduces new privacy and security challenges. We propose ByITFL and LoByITFL, two novel FL schemes that enhance resilience against Byzantine users while keeping the users' data private from eavesdroppers. To ensure...

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments

With the growing integration of vision-language models VLMs, mobile agents are now widely used for tasks like UI automation and camera-based user assistance. These agents are often fine-tuned on limited user-generated datasets, leaving them vulnerable to covert threats during the training process...

Characterising Bugs in Jupyter Platform

As a representative literate programming platform, Jupyter is widely adopted by developers, data analysts, and researchers for replication, data sharing, documentation, interactive data visualization, and more. Understanding the bugs in the Jupyter platform is essential for ensuring its...

From Permissioned to Proof-of-Stake Consensus

This paper presents the first generic compiler that transforms any permissioned consensus protocol into a proof-of-stake permissionless consensus protocol. For each of the following properties, if the initial permissioned protocol satisfies that property in the partially synchronous setting, the...

Human-Centred AI in FinTech: Developing a User Experience (UX) Research Point of View (PoV) Playbook

Advancements in Artificial Intelligence AI have significantly transformed the financial industry, enabling the development of more personalized and adaptable financial products and services. This research paper explores various instances where Human-Centred AI HCAI has facilitated these...

Side-Channel Extraction of Dataflow AI Accelerator Hardware Parameters

Dataflow neural network accelerators efficiently process AI tasks on FPGAs, with deployment simplified by ready-to-use frameworks and pre-trained models. However, this convenience makes them vulnerable to malicious actors seeking to reverse engineer valuable Intellectual Property IP through...

LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops

Multimodal Large Language Models MLLMs have shown great promise but require substantial computational resources during inference. Attackers can exploit this by inducing excessive output, leading to resource exhaustion and service degradation. Prior energy-latency attacks aim to increase generatio...

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning

Despite federated learning FL's potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning CFL has emerged to address this challenge by partitioning users into clusters according to their...

Manipulated Regions Localization for Partially Deepfake Audio: a Survey

With the development of audio deepfake techniques, attacks with partially deepfake audio are beginning to rise. Compared to fully deepfake, it is much harder to be identified by the detector due to the partially cryptic manipulation, resulting in higher security risks. Although some studies have...

Efficient Retail Video Annotation: a Robust Key Frame Generation Approach for Product and Customer Interaction Analysis

Accurate video annotation plays a vital role in modern retail applications, including customer behavior analysis, product interaction detection, and in-store activity recognition. However, conventional annotation methods heavily rely on time-consuming manual labeling by human annotators,...

Linear and Numerical SDoF Bounds of Active RIS-Assisted MIMO Wiretap Interference Channel

The multiple-input multiple-output MIMO wiretap interference channel IC serves as a canonical model for information-theoretic security, where a multiple-antenna eavesdropper attempts to intercept communications in a two-user MIMO IC system. The secure degrees-of-freedom SDoF of an active...

Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices

The integration of AI/ML into medical devices is rapidly transforming healthcare by enhancing diagnostic and treatment facilities. However, this advancement also introduces serious cybersecurity risks due to the use of complex and often opaque models, extensive interconnectivity, interoperability...

Building Automotive Security on Internet Standards: an Integration of DNSSEC, DANE, and DANCE to Authenticate and Authorize In-Car Services

The automotive industry is undergoing a software-as-a-service transformation that enables software-defined functions and post-sale updates via cloud and vehicle-to-everything communication. Connectivity in cars introduces significant security challenges, as remote attacks on vehicles have become...

Weakest Link in the Chain: Security Vulnerabilities in Advanced Reasoning Models

The introduction of advanced reasoning capabilities have improved the problem-solving performance of large language models, particularly on math and coding benchmarks. However, it remains unclear whether these reasoning models are more or less vulnerable to adversarial prompt attacks than their...

Consensus Power Inequality: a Comparative Study of Blockchain Networks

The distribution of consensus power is a cornerstone of decentralization, influencing the security, resilience, and fairness of blockchain networks while ensuring equitable impact among participants. This study provides a rigorous evaluation of consensus power inequality across five prominent...

Real-Time, Low-Latency Surveillance Using Entropy-Based Adaptive Buffering and MobileNetV2 on Edge Devices

This paper describes a high-performance, low-latency video surveillance system designed for resource-constrained environments. We have proposed a formal entropy-based adaptive frame buffering algorithm and integrated that with MobileNetV2 to achieve high throughput with low latency. The system is...

Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems

Sensitive data captured by Industrial Control Systems ICS play a large role in the safety and integrity of many critical infrastructures. Detection of anomalous or malicious data, or Anomaly Detection AD, with machine learning is one of many vital components of cyberphysical security. Quantum...

MECHA: Multithreaded and Efficient Cryptographic Hardware Access

This paper presents a multithread and efficient cryptographic hardware access MECHA for efficient and fast cryptographic operations that eliminates the need for context switching. Utilizing a UNIX domain socket, MECHA manages multiple requests from multiple applications simultaneously, resulting ...

Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers

We study privacy leakage in the reasoning traces of large reasoning models used as personal agents. Unlike final outputs, reasoning traces are often assumed to be internal and safe. We challenge this assumption by showing that reasoning traces frequently contain sensitive user data, which can be...

One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning

Face sketch synthesis is a technique aimed at converting face photos into sketches. Existing face sketch synthesis research mainly relies on training with numerous photo-sketch sample pairs from existing datasets. However, these large-scale discriminative learning methods will have to face proble...

A TRNG Implemented Using a Soft-Data Based Sponge Function within a Unified Strong PUF Architecture

Hardware security primitives including True Random Number Generators TRNG and Physical Unclonable Functions PUFs are central components to establishing a root of trust in microelectronic systems. In this paper, we propose a unified PUF-TRNG architecture that leverages a combination of the static...

LLM vs. SAST: a Technical Analysis on Detecting Coding Bugs of GPT4-Advanced Data Analysis

With the rapid advancements in Natural Language Processing NLP, large language models LLMs like GPT-4 have gained significant traction in diverse applications, including security vulnerability scanning. This paper investigates the efficacy of GPT-4 in identifying software vulnerabilities compared...

Using LLMs for Security Advisory Investigations: How Far Are We?

Large Language Models LLMs are increasingly used in software security, but their trustworthiness in generating accurate vulnerability advisories remains uncertain. This study investigates the ability of ChatGPT to 1 generate plausible security advisories from CVE-IDs, 2 differentiate real from fa...

On Secure UAV-Aided ISCC Systems

Integrated communication and sensing, which can make full use of the limited spectrum resources to perform communication and sensing tasks simultaneously, is an up-and-coming technology in wireless communication networks. In this work, we investigate the secrecy performance of an uncrewed aerial...

Narrowing the Gap between TEEs Threat Model and Deployment Strategies

Confidential Virtual Machines CVMs provide isolation guarantees for data in use, but their threat model does not include physical level protection and side-channel attacks. Therefore, current deployments rely on trusted cloud providers to host the CVMs' underlying infrastructure. However, TEE...

EVA-S2PMLP: Secure and Scalable Two-Party MLP via Spatial Transformation

Whitepaper called EVA-S2PMLP: Secure and Scalable Two-Party MLP via Spatial Transformation...

From LLMs to MLLMs to Agents: a Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem

Large language models LLMs are rapidly evolving from single-modal systems to multimodal LLMs and intelligent agents, significantly expanding their capabilities while introducing increasingly severe security risks. This paper presents a systematic survey of the growing complexity of jailbreak...

Advanced Prediction of Hypersonic Missile Trajectories with CNN-LSTM-GRU Architectures

Advancements in the defense industry are paramount for ensuring the safety and security of nations, providing robust protection against emerging threats. Among these threats, hypersonic missiles pose a significant challenge due to their extreme speeds and maneuverability, making accurate trajecto...

Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles

As Internet of Vehicles IoV technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of offloading tasks to edge servers may expose vehicles to malicious external attacks, resulting in information loss or...

A Dual-Layer Image Encryption Framework Using Chaotic AES with Dynamic S-Boxes and Steganographic QR Codes

This paper presents a robust image encryption and key distribution framework that integrates an enhanced AES-128 algorithm with chaos theory and advanced steganographic techniques for dual-layer security. The encryption engine features a dynamic ShiftRows operation controlled by a logistic map,...

A Theory of Lending Protocols in DeFi

Lending protocols are one of the main applications of Decentralized Finance DeFi, enabling crypto-assets loan markets with a total value estimated in the tens of billions of dollars. Unlike traditional lending systems, these protocols operate without relying on trusted authorities or off-chain...

An Advanced Reliability Reserve Incentivizes Flexibility Investments While Safeguarding the Electricity Market

To ensure security of supply in the power sector, many countries are already using or discussing the introduction of capacity mechanisms. Two main types of such mechanisms include capacity markets and capacity reserves. Simultaneously, the expansion of variable renewable energy sources increases...

CipherMind: the Longest Codebook in the World

In recent years, the widespread application of large language models has inspired us to consider using inference for communication encryption. We therefore propose CipherMind, which utilizes intermediate results from deterministic fine-tuning of large model inferences as transmission content. The...

SoK: Advances and Open Problems in Web Tracking

Web tracking is a pervasive and opaque practice that enables personalized advertising, retargeting, and conversion tracking. Over time, it has evolved into a sophisticated and invasive ecosystem, employing increasingly complex techniques to monitor and profile users across the web. The research...

PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning

Decentralized learning is vulnerable to poison attacks, where malicious clients manipulate local updates to degrade global model performance. Existing defenses mainly detect and filter malicious models, aiming to prevent a limited number of attackers from corrupting the global model. However,...

CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets

In the ever-expanding domain of 5G-NR wireless cellular networks, over-the-air jamming attacks are prevalent as security attacks, compromising the quality of the received signal. We simulate a jamming environment by incorporating additive white Gaussian noise AWGN into the real-world In-phase and...

Toward a Lightweight, Scalable, and Parallel Secure Encryption Engine

The exponential growth of Internet of Things IoT applications has intensified the demand for efficient, high-throughput, and energy-efficient data processing at the edge. Conventional CPU-centric encryption methods suffer from performance bottlenecks and excessive data movement, especially in...

Now More Than Ever, Foundational AI Research and Infrastructure Depends on the Federal Government

Leadership in the field of AI is vital for our nation's economy and security. Maintaining this leadership requires investments by the federal government. The federal investment in foundation AI research is essential for U.S. leadership in the field. Providing accessible AI infrastructure will...

Unlearning-Enhanced Website Fingerprinting Attack: against Backdoor Poisoning in Anonymous Networks

Website Fingerprinting WF is an effective tool for regulating and governing the dark web. However, its performance can be significantly degraded by backdoor poisoning attacks in practical deployments. This paper aims to address the problem of hidden backdoor poisoning attacks faced by Website...